标题： EndoFlow-SLAM：具有流约束高斯点云的实时内窥镜SLAM 显示英文标题

标题： EndoFlow-SLAM: Real-Time Endoscopic SLAM with Flow-Constrained Gaussian Splatting

摘要： 在内窥镜等手术场景中，高效的三维重建和实时可视化至关重要。近年来，三维高斯点云（3DGS）在高效的三维重建和渲染方面表现出色。大多数基于3DGS的同步定位与地图构建（SLAM）方法仅依赖外观约束来优化3DGS和相机位姿。然而，在内窥镜场景中，挑战包括由非朗伯表面引起的光度不一致以及呼吸引起的动态运动，这些都会影响SLAM系统的性能。为了解决这些问题，我们额外引入了光流损失作为几何约束，这能有效约束场景的三维结构和相机运动。此外，我们提出了一种深度正则化策略，以减轻光度不一致的问题，并确保内窥镜场景中3DGS深度渲染的有效性。此外，为了提高SLAM系统中的场景表示，我们通过专注于关键帧中渲染质量较差的视图来改进3DGS优化策略，从而实现了更好的渲染结果。在C3VD静态数据集和StereoMIS动态数据集上的大量实验表明，我们的方法在新视角合成和位姿估计方面优于现有的最先进方法，在静态和动态手术场景中均表现出高性能。 显示英文摘要

摘要： Efficient three-dimensional reconstruction and real-time visualization are critical in surgical scenarios such as endoscopy. In recent years, 3D Gaussian Splatting (3DGS) has demonstrated remarkable performance in efficient 3D reconstruction and rendering. Most 3DGS-based Simultaneous Localization and Mapping (SLAM) methods only rely on the appearance constraints for optimizing both 3DGS and camera poses. However, in endoscopic scenarios, the challenges include photometric inconsistencies caused by non-Lambertian surfaces and dynamic motion from breathing affects the performance of SLAM systems. To address these issues, we additionally introduce optical flow loss as a geometric constraint, which effectively constrains both the 3D structure of the scene and the camera motion. Furthermore, we propose a depth regularisation strategy to mitigate the problem of photometric inconsistencies and ensure the validity of 3DGS depth rendering in endoscopic scenes. In addition, to improve scene representation in the SLAM system, we improve the 3DGS refinement strategy by focusing on viewpoints corresponding to Keyframes with suboptimal rendering quality frames, achieving better rendering results. Extensive experiments on the C3VD static dataset and the StereoMIS dynamic dataset demonstrate that our method outperforms existing state-of-the-art methods in novel view synthesis and pose estimation, exhibiting high performance in both static and dynamic surgical scenes.